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26 results for “water velocity”
3-D velocity field of the Tibetan Plateau due to land water loading
<h3>Basic information:</h3> <p>This dataset includes a series of 3-D loading deformation velocity fields, which are supplements to the GRL paper entitled "Present-Day Three-Dimensional Crustal Deformation Velocity of the Tibetan Plateau Due to Multi-Component Land Water Loading" [<a href="https://doi.org/10.1029/2024GL108684">https://doi.org/10.1029/2024GL108684</a>]. The deformation velocities are fitted using long time span data during 2000-2020, and the detailed description of the data processing and calculation methods can be found through the GRL paper. There are results of three different grid resolutions (0.5x0.5, 0.25x0.25, 0.1x0.1), and for distinction, different file naming suffixes are used. For example, '0point5grids' indicates the results are in 0.5-degree grid resolutions (0.5x0.5), and so forth.</p> <h3>Application scenario:</h3> <p>The velocity fields here can be directly used for the analysis of crustal deformation or used for the separation of land water-induced loading deformation within geodetic deformation velocity fields over Tibetan Plateau. There are results of all the six main land water components, including soil moisture (SM) [Table S1], snow water equivalent (SWE) [Table S2], glacier [Table S3], lake [Table S4], permafrost (PM) [Table S5] and groundwater storage (GWS) [Table S6], thus users can choose one or some they focus on, or directly choose the sum of all the six main components (i.e., GRACE-inferred total terrestrial water storage [Table S7]).</p> <h3>Citation: </h3> <p>When using this dataset, please cite the GRL paper: Jiao, J., Pan, Y., Ren, D., & Zhang, X. (2024). Present-day three-dimensional crustal deformation velocity of the Tibetan Plateau due to multi-component land water loading. <em>Geophysical Research Letters</em>, 51, e2024GL108684. <a href="https://doi.org/10.1029/2024GL108684">https://doi.org/10.1029/2024GL108684</a></p> <h3>Contents:</h3> <p>Table S1. 3-D velocity field of the Tibetan Plateau due to the loading of soil moisture (SM).</p> <p>Table S2. 3-D velocity field of the Tibetan Plateau due to the loading of snow water equivalent (SWE).</p> <p>Table S3. 3-D velocity field of the Tibetan Plateau due to the loading of glacier.</p> <p>Table S4. 3-D velocity field of the Tibetan Plateau due to the loading of lake.</p> <p>Table S5. 3-D velocity field of the Tibetan Plateau due to the loading of permafrost (PM).</p> <p>Table S6. 3-D velocity field of the Tibetan Plateau due to the loading of groundwater storage (GWS).<br>Table S7. 3-D velocity field of the Tibetan Plateau due to the loading of GRACE-inferred total terrestrial water storage (TWS).</p>
Water flow velocity data, Shark River Slough (SRS) near Black Hammock island, Everglades National Park (FCE LTER), South Florida from October 2003 to August 2005
Water velocity data measured every 5 or 15 minutes in Shark River Slough beside Black Hammock tree island, Everglades National Park using Sontek Agronaut water flow sampler.
Water flow velocity data, Shark River Slough (SRS) near Chekika tree island, Everglades National Park (FCE LTER) from January 2006 to March 2021
Water velocity data measured every 5 or 15 minutes in Shark River Slough beside Chekika tree island, Everglades National Park, using Sontek Agronaut water flow sampler. Data collection is complete.
Water flow velocity data, Shark River Slough (SRS) near Frog City, south of US 41, Everglades National Park (FCE LTER) from October 2006 to July 2009
Water velocity data measured every 5 or 15 minutes in Shark River Slough near Frog City jetty, Everglades National Park, using Sontek Agronaut water flow sampler.
Water flow velocity data, Shark River Slough (SRS) near Gumbo Limbo Island, Everglades National Park (FCE) from October 2003 - December 2018
Water velocity data measured every 5 or 15 minutes in Shark River Slough near Gumbo Limbo Island, Everglades National Park, using Sontek Agronaut water flow sampler. Data collection is complete.
Water flow velocity data, Shark River Slough (SRS) near Satinleaf Island, Everglades National Park (FCE LTER) from July 2003 to December 2005
Water velocity data measured every 5 or 15 minutes in Shark River Slough near Satinleaf tree island, Everglades National Park, using Sontek Agronaut water flow sampler.
Lagrangian Water Age trajectories initiated from the coastal 500m isobath and derived from surface velocities obtained from satellite observations
We conduct a Lagrangian particle trajectory analysis of surface velocities. We define an “offshore water age” as the time taken by a water parcel to be advected backward in time from its current position along its trajectory until it crosses the 500 m isobath. The rationale of this diagnostic is to detect filaments of coastal water advected offshore by horizontal transport and to estimate the time for water parcels in the filament o leave the coastal area. For example, a value of “20 days” assigned to a pixel means that the water parcel in that area was in the coastal area approximately 20 days before, where it was likely enriched in nutrients.
Full Inverse Velocity Fields for "Transport of Antarctic Bottom Water entering the Brazil Basin in a Planetary Geostrophic Inverse Model"
<p>Velocity fields on all approximate neutral surfaces from the inverse model presented in "Transport of Antarctic Bottom Water entering the Brazil Basin in a Planetary Geostrophic Inverse Model". The pressure of the approximate neutral surface is contoured in the background. The number in the title represents the pressure of the approximate neutral surface at the reference station in the Hunter Channel.</p>
Eddy flux measurements and transfer velocities of momentum, water vapor, and sulfur dioxide over the coastal Atlantic ocean
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Air-sea gas transfer velocities measured at wind speeds up to 85m/s in fresh water and seawater
<p>This data set contains gas transfer velocities of 12 tracers (CF4, He, SF6, He, Kr, Pentafluoroethane, Xe, Acetylene, Hexafluorobenzene, Difluoromethane, 1,4-Difluorobenzene, Dimethyl Sulfide, Methyl Acetate) measured in the Kyoto High Speed Wind-Wave tank with fresh water and modeled seawater and the Miami SUSTAIN wind-wave tank with seawater at wind speeds up to 85m/s.</p> <p> </p>
Sliding velocity, water discharge and basal shear stress time series at Argentière Glacier
<p>The data set contains all data presented in:</p> <p>Gimbert, F., Gilbert, A., Gagliardini, O., Vincent, C., & Moreau, L. (2021). Do Existing Theories Explain Seasonal to Multi-Decadal Changes in Glacier Basal Sliding Speed? <em>Geophysical Research Letters</em>, <em>48</em>(15), e2021GL092858. <a href="https://doi.org/10.1029/2021GL092858">https://doi.org/10.1029/2021GL092858</a></p> <p>and also in:</p> <p>Gilbert, A., Gimbert, F., Thøgersen, K., Schuler, T. V., & Kääb, A. (2022). A Consistent Framework for Coupling Basal Friction with Subglacial Hydrology on Hard-bedded Glaciers. <em>Geophysical Research Letters</em>, <em>49</em>, e2021GL097507. <a href="https://doi.org/10.1029/2021GL097507">https://doi.org/10.1029/2021GL097507</a></p> <p>Files Description:</p> <p>==================================<br> SlidingVelocities1989_2019.csv :<br> ==================================</p> <p>Contains daily values of recorded sliding velocities at the wheel.</p> <p>Column 1 = Date<br> Column 2 = Daily Values (cm/day)</p> <p>================================<br> BasalShearStress1980_2019.csv :<br> ================================</p> <p>Contains daily values of inferred basal shear stress at the wheel.</p> <p>Column 1 = Date<br> Column 2 = Daily Values (MPa)</p> <p>================================<br> WaterDischarge1985_2019.csv :<br> ================================</p> <p>Contains daily values of recorded water discharge at the glacier outlet</p> <p>Column 1 = Date<br> Column 2 = Daily Values (m3/s)</p>
Grain size distribution of Amazon river sediment samples collected over the period 2005-2008; and ADCP water velocity profiles collected on the major tributaries of the Amazon in Bolivia and Peru, 2007-2008
<p>This dataset contains two items:</p> <p>- The grain size distribution of river sediment samples collected along the Amazon River and its tributaries during four sampling campaigns performed in June 2005 (lower Amazon, Brazil), March 2006 (lower Amazon, Brazil), May 2007 (Upper Madeira, Bolivia), and April 2008 (Upper Solimões-Amazonas, Peru). [spreadsheet "Grain_size_distribution_dataset_Amazon_2005-2008_Bouchez_data.xlsx"].</p> <p>- River water velocity profiles derived from Acoustic Doppler Current Profiler (ADCP) measurements performed on the major tributaries of the Amazon in May 2007 (Upper Madeira, Bolivia) and April 2008 (Upper Solimões-Amazonas, Peru) [folder "ADCP_dataset_Amazon_2007-2008_Bouchez_data"].</p> <p>The dataset description and the relevant references are provided in the text files "Grain_size_distribution_dataset_Amazon_2005-2008_Bouchez_description.docx" and "ADCP_dataset_Amazon_2007-2008_Bouchez_description.docx" .</p> <p>These data were acquired thanks to the support of the French National Service for Observation "HYBAM" ("Hydrogeochemistry of the Amazon Basin"), part of the CNRS National Infrastructure "OZCAR" ("Critical Zone Observatories: Applications and Research").</p>
Dataset: Transient subglacial water routing efficiency modulates ice1 velocities prior to surge termination on Sít' Kusá, AK
<p>These files complement the data in <em>Transient subglacial water routing efficiency modulates ice1</em><br><em>velocities prior to surge termination on Sít’ Kusá, AK, </em>submitted to Journal of Glaciology. </p> <p>They contain the following: </p> <ul> <li>SK_runoff.csv: spatially integrated surface runoff modelled from climatic variables with the Energy Balance Firn Model (https://github.com/wardvp/EBFM-glacier). File header gives further detailed information, including location coordinates for which the surface runoff is computed.</li> <li>SK_GHT_3_10Hz.csv: file containing the median tremor time series summed between 3 and 10 Hz, which we associate with glaciohydraulic tremor. </li> <li>SK_lags_GHT.csv: file containing the 3-6 day period lag times between station pairs, when GHT signal coherence is >0.7, computed with wavelet coherence as outlined in the paper. </li> </ul> <p> </p>
Figure 2. Velocity field U, t in Reconstruction of a passive tracer boundary source in an open water area
Figure 2. Velocity field U, t = 0.
Mooring Q1 and Q2 sea water velocity
<p>These data were extracted from two moorings located on the north slope and center of the Mariana Trench, respectively.</p>
Data from: Ontogenetic shape trajectory of Trichomycterus areolatus varies in response to water velocity environment
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Eddy flux measurements and transfer velocities of momentum, sensible heat, water vapor, and sulfur dioxide at Scripps Pier
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Temperature, water velocity and heat consumption of a heat network in southern Germany
<p>This is a heat network dataset from the Institute of Space Propulsion (DLR) in southern Germany. The data was collected every 15 minutes from meters installed at the site of each building (September 2021 - May 2022). The records show the temperature at and after the heat supply, the water velocity and the thermal power.</p>
Sliding velocity, water discharge, water pressure, and rainfall time series at Argentière Glacier between 2019 and 2021
<p>Files Description:</p> <p>==================================<br>cavitometer_2019-2021.dat:<br>==================================</p> <p>Contains 30-min sampled values of recorded sliding velocities at the cavitometer.</p> <p>Column 1 = Date<br>Column 2 = Velocity (mm/hour)</p> <p>================================<br>water_discharge_2019-2021.dat:<br>================================</p> <p>Contains 15-min sampled values of recorded water discharge at the glacier outlet.</p> <p>Column 1 = Date<br>Column 2 = Water discharge (m3/s)</p> <p>================================<br>water_pressure_2019-2021.dat:<br>================================</p> <p>Contains 30-min sampled values of recorded water pressure at the borehole BH2.</p> <p>Column 1 = Date<br>Column 2 = Water pressure (bar)</p> <p>================================<br>rainfall_2019-2021.dat:<br>================================</p> <p>Contains 30-min sampled values of recorded rainfall at the meteo station.</p> <p>Column 1 = Date<br>Column 2 = Rainfall (mm w.eq./hour)</p>
Liquid water content and vertical velocity from RICO-based LES simulations
<p>The RICO-based simulations by implementing the LES module of WRF model 4.0 generate the raw data. The simulation continued for 40 hours with a time step of 0.5 second. The domain size is 12.8×12.8×4 km<sup>3</sup>, with a resolution of 50 m and 40 m in the horizontal and vertical orientation, respectively. The outputs during 8~40 hour were retained every 20 minutes. Two parameters (LWC and vertical velocity) were then extracted by using MATLAB to create the following dataset. Additionally, LWC at 1 min before each moment was also uploaded for tracking. The "q" and "w" in the filename represent LWC and vertical velocity, respectively, and the number in the filename represents the corresponding hour and minute (e.g. "q24_20" means LWC value at 24h20m). The unit of "q" and "w" are "kg/kg" and "m/s' respectively.</p>
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International Brain Laboratory public data
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OpenNeuro
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